On the Utility of Pretraining Language Models on Synthetic Data
Bibliographic record
Abstract
Development of pre-trained language models has predominantly relied on large amounts of datasets.However, this dependence on abundant data has limited the applicability of these models in low-resource settings.In this work, we investigate the utility of exploiting synthetic datasets acquired from different sources to pretrain language models for Arabic.Namely, we leverage data derived based on four different methods: optical character recognition (OCR), automatic speech recognition (ASR), machine translation (MT), and generative language models.We use these datasets to pre-train models in three different architectures: encoderonly (BERT Base ), encoder-decoder (T5), and decoder-only (GPT-2).We test the capabilities of resulting models on Arabic natural language understanding (NLU) tasks using the ORCA benchmark.Our results show that utilizing synthetic data can achieve performance comparable to, or even surpassing, those trained on gold data.For example, our model based on a GPT-2 architecture trained on a combined synthetic dataset surpasses the baseline model ARBERT v2 .Overall, our models pre-trained on synthetic data demonstrate robust performance across various tasks.This highlights the potential of synthetic datasets in augmenting language model training in low-resource settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".